Modeling and analysis of energy harvesting nodes in wireless sensor networks

Seyedi, Alireza; Sikdar, Biplab

doi:10.1109/allerton.2008.4797537

Cited by 49 publications

(31 citation statements)

References 7 publications

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“…Alireza Seyedi et.al. in 2008 proposed a unified model based on Markov chain for energy harvesting nodes in [5]. It provided an approximation of probability of event of loss of energy.…”

Section: Related Workmentioning

confidence: 99%

Twin Policy Management Model for Energy Harvesting Wireless Sensor Networks

Jain¹,

Goyal²,

Prasad³

2017

IJCA

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This paper proposes a twin policy model to manage energy for a prolonged network lifetime of a sensor network. Based on the total remaining energy and Probability index calculation to harvest energy in the next round, and an expected energy achieving constant it decides which policy is to be implemented. It is also proposed that a switch between continuous model and partially event triggered model of data reporting. First Policy makes the network to work on continuous model and second policy governs the network for event triggered model. This paper uses a heterogeneous model where a small fraction of nodes have harvesting potential to harvest ambient energy. A multi-hop data forwarding model is used for the communication of data to the sink and to forward command to the nodes.

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“…Alireza Seyedi et.al. in 2008 proposed a unified model based on Markov chain for energy harvesting nodes in [5]. It provided an approximation of probability of event of loss of energy.…”

Section: Related Workmentioning

confidence: 99%

Twin Policy Management Model for Energy Harvesting Wireless Sensor Networks

Jain¹,

Goyal²,

Prasad³

2017

IJCA

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“…A first-order Markov chain is used in [24] to generate solar irradiance predictions. A similar first-order Markov chain approach is adopted in [25] which also incorporates the concept of active and inactive states to achieve improved performance. MAKERS is another stochastic model [26], which utilizes first-order Markov chains to generate residual energy predictions for Body Sensor Networks (BSN) which constitute a promising network paradigm.…”

Section: Stochastic Modelsmentioning

confidence: 99%

“…One stochastic process commonly used is Markov chains [24,25]. A first-order Markov chain is used in [24] to generate solar irradiance predictions.…”

Section: Stochastic Modelsmentioning

confidence: 99%

“…Energy harvesting enabled systems can be better managed when effective energy prediction models are readily available. For this reason, a number of energy prediction models have appeared in literature [15][16][17][18][19][20][21][22][23][24][25][26][27][28]. The principle aim of this work is to review recently proposed harvested energy prediction schemes and provide a comparative study against landmark solutions which appear in the literature in order to investigate the relative advantages of each policy.…”

Section: Introductionmentioning

confidence: 99%

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Harvested Energy Prediction Schemes for Wireless Sensor Networks: Performance Evaluation and Enhancements

Muhammad

Qureshi

Saleem

et al. 2017

Wireless Communications and Mobile Computing

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We review harvested energy prediction schemes to be used in wireless sensor networks and explore the relative merits of landmark solutions. We propose enhancements to the well-known Profile-Energy (Pro-Energy) model, the so-called Improved Profile-Energy (IPro-Energy), and compare its performance with Accurate Solar Irradiance Prediction Model (ASIM), Pro-Energy, and Weather Conditioned Moving Average (WCMA). The performance metrics considered are the prediction accuracy and the execution time which measure the implementation complexity. In addition, the effectiveness of the considered models, when integrated in an energy management scheme, is also investigated in terms of the achieved throughput and the energy consumption. Both solar irradiance and wind power datasets are used for the evaluation study. Our results indicate that the proposed IPro-Energy scheme outperforms the other candidate models in terms of the prediction accuracy achieved by up to 78% for short term predictions and 50% for medium term prediction horizons. For long term predictions, its prediction accuracy is comparable to the Pro-Energy model but outperforms the other models by up to 64%. In addition, the IPro scheme is able to achieve the highest throughput when integrated in the developed energy management scheme. Finally, the ASIM scheme reports the smallest implementation complexity.

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“…However, while data is measured in terms of discrete packets, energy is quantified or measured in continuous units. Our aim is to provide a fine-grained mathematical framework for optimizing the performance of these systems, which complements recent coarse-grained approaches based on queueing theory [4]- [8], or based on fluid flow approximations [9], [10] that may not capture adequately the stochastic nature of energy availability.…”

Section: Introductionmentioning

confidence: 99%

A Diffusion Model for Energy Harvesting Sensor Nodes

Abdelrahman

Gelenbe

2016

2016 IEEE 24th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS)

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Abstract-Energy harvesting has recently attracted much interest due to the emergence of the Internet of Things, and the increasing need to operate wireless sensing devices in challenging environments without much human intervention and maintenance. This paper presents a novel approach for modeling the performance of an energy harvesting wireless sensor node, which takes into account fluctuations in the amount of energy extracted from the environment, energy loss due to battery leakage, as well as the energy cost of sensing, data processing and communication. The proposed approach departs from the common queueing-theoretic framework used in the literature, and instead uses Brownian motion to represent more accurately the time evolution of the distribution of the node's battery level. The paper derives some performance measures of interest along with the stationary solution of the system, and discusses possible directions for reducing the number of parameters and states of the model without compromising accuracy.

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Modeling and analysis of energy harvesting nodes in wireless sensor networks

Cited by 49 publications

References 7 publications

Twin Policy Management Model for Energy Harvesting Wireless Sensor Networks

Twin Policy Management Model for Energy Harvesting Wireless Sensor Networks

Harvested Energy Prediction Schemes for Wireless Sensor Networks: Performance Evaluation and Enhancements

A Diffusion Model for Energy Harvesting Sensor Nodes

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